code: def test_correct_results(self, _, in_type, keys, mean, std):
      rician_fn = RandRicianNoised(
          keys=keys, prob=1.0, mean=mean, std=std, dtype=np.float64
      )
      rician_fn.set_random_state(seed)
      noised = rician_fn({k: in_type(self.imt) for k in keys})
      np.random.seed(seed)
      for k in keys:
          # simulate the `randomize` function of transform
          np.random.random()
          _std = np.random.uniform(0, std)
          expected = np.sqrt(
              (
                  self.imt
                  + np.random.normal(mean, _std, size=self.imt.shape)
              )
              ** 2
              + np.random.normal(mean, _std, size=self.imt.shape) ** 2
          )
          if isinstance(noised[k], torch.Tensor):
              noised[k] = noised[k].cpu()
          np.testing.assert_allclose(
              expected, noised[k], atol=1e-5, rtol=1e-5
          )
  
file_path: MONAI/tests/test_rand_rician_noised.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai.transforms import RandRicianNoised
  from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D
label_desc: Use image node_id 1 for calling the TestRandRicianNoisedNumpy obj's underlying member method code with example usage: obj.test_correct_results(_, in_type, keys, mean, std) without return types